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%load_ext autoreload
%autoreload 2
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import os
MOMAPS_HOME = '/home/labs/hornsteinlab/Collaboration/MOmaps_Noam/MOmaps'
MOMAPS_DATA_HOME = '/home/labs/hornsteinlab/Collaboration/MOmaps'
LOGS_PATH = os.path.join(MOMAPS_DATA_HOME, 'src', 'preprocessing', 'logs','np')
PLOT_PATH = os.path.join(MOMAPS_HOME, 'src', 'preprocessing', 'notebooks','figures','NP')
os.chdir(MOMAPS_HOME)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="whitegrid", font_scale=1.5)
sns.color_palette("husl", 8)
plt.rcParams["image.cmap"] = "Set1"
from tqdm.notebook import tqdm
from src.common.lib.preprocessing_utils import rescale_intensity
from src.common.lib.images_qc import *
#sys.path.insert(1, "/home/labs/hornsteinlab/Collaboration/MOmaps_Sagy/MOmaps/src/common/lib")
import contextlib
import io
import matplotlib
import warnings
warnings.filterwarnings('ignore', category=pd.core.common.SettingWithCopyWarning)
from src.common.lib.qc_config_tmp import *
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df = log_files_qc(LOGS_PATH)
validate folder structure and files existence¶
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# choose batches
batches = [f'batch{i}' for i in range (1,5)]
#batches=['batch5']
Raw Files¶
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root_directory_raw = os.path.join(MOMAPS_DATA_HOME, 'input', 'images', 'raw', 'SpinningDisk','NiemannPick_sort')
raws = run_validate_folder_structure(root_directory_raw, False, np_panels, np_markers,PLOT_PATH,
np_marker_info, np_cell_lines_to_cond, reps,
np_cell_lines_for_disp,
np_expected_dapi_raw, batches=batches,
fig_width=4, fig_height=10)
Processed¶
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root_directory_proc = os.path.join(MOMAPS_DATA_HOME, 'input', 'images', 'processed', 'spd2',
'SpinningDisk','NiemannPick')
procs = run_validate_folder_structure(root_directory_proc, True, np_panels,
np_markers,PLOT_PATH,
np_marker_info, np_cell_lines_to_cond, reps,
np_cell_lines_for_disp,
np_expected_dapi_raw, batches=batches,
fig_width=4, fig_height=10)
Difference between Raw and Processed¶
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display_diff(batches, raws, procs, PLOT_PATH, 10,4)
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for batch in batches:
with contextlib.redirect_stdout(io.StringIO()):
var = sample_and_calc_variance(root_directory_proc, batch,
sample_size_per_markers=200,
num_markers=len(np_markers),
cond_count=2, rep_count=len(reps))
print(f'{batch} var: ',var)
Number of sites in each batch and cell line ¶
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plot_sites_count(df, np_expected_raw, np_lines_order, np_custom_palette, split_to_reps=True)
Number of Cells in Site for each batch and cell line ¶
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df_no_empty_sites = df[df.n_valid_tiles !=0]
plot_cell_count(df_no_empty_sites, np_lines_order, np_custom_palette, whole_cells=True)
plot_cell_count(df_no_empty_sites, np_lines_order, np_custom_palette, whole_cells=False)
# can add norm=True to norm by max
number of valid tiles per image (site)¶
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plot_n_valid_tiles_count(df, custom_palette, reps, batch_min=2, batch_max=5)
Heatmap QC per batch, panel and cell line(tiles that passed QC condition) ¶
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plot_hm(df, split_by='rep', rows='cell_line', columns='panel')
Assessing Staining Reproducibility and Outliers¶
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for batch in batches:
print(batch)
run_calc_hist_new(f'NiemannPick_sort/{batch}',np_cell_lines_for_disp,
np_markers,hist_sample=10,ncols=2, nrows=2,
cond_count=2, rep_count=len(reps),
sample_size_per_markers=200)
print("="*30)
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# save notebook as HTML ( the HTML will be saved in the same folder the original script is)
from IPython.display import display, Javascript
display(Javascript('IPython.notebook.save_checkpoint();'))
os.system('jupyter nbconvert --to html src/preprocessing/notebooks/cell_count_stats_analysis_NP.ipynb')
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